This course enables you to transform data into persuasive and evidence-based visualizations. Visualizations are persuasive if they motivate actions in an intended audience. Visualizations are evidence-based if they are reproducible, functional, and truthful.
This course introduces and discusses the fundamental design principles and visualization technology that allows you to design, implement, and critique persuasive and evidence-based visualizations. In a data-rich environment, where decision-makers often drown in data but thirst for insight 1, mastering this course equips you with a moderate level of data literacy.
Data literacy is the ability to interpret, construct, and convey arguments through the functional and truthful visual presentation of data. Data literacy is a vital skill in our data-driven world. The chances are high that you will be interpreting and designing data visualizations throughout your career. The level of data literacy offered through this course allows you to establish a competitive advantage in Silicon Valley and the global marketplace.
You will learn to design and implement visualizations and critique the persuasiveness and evidence of visualizations. Upon successful completion of this course you will:
There is no required textbook for this class. Data visualization is still a fluid topic that is covered in arts, design, and technology. You will find a lot of “conventional wisdom” out there (including in the books below). Please consume information with a critical mind.
I consider the following books as the my ‘common core’ of contemporary data visualization:
The philosophy of data visualization:
The concepts of data visualization:
The technology of data visualization:
The practice of data visualization:
The hands-on elements in this course use Tableau Desktop, Jupyter/Python, R, and D3.js. I believe that experiencing data visualization through a variety of technology helps you to identify commonalities and important differences.
In the class meetings, we will work with all of these technologies. In the assignments, you may choose your favorite set of technologies. In your assignments, you are also free to use other technologies. Please discuss such plans with me prior to using other technologies.
Learn how to install these technologies in the FAQ document of this course (accessible only with SCU ID). If you run into any issues, please write your issue or question also in the FAQ document of this course (accessible only with SCU ID).
PLEASE NOTE: I expect you to have Tableau Desktop, Jupyter/Python, and R Studio installed on your laptop.
It is my goal to spend the classroom time on conceptual and hands-on issues of data visualization. Therefore, we will only spend a minimal amount of time explaining how to use Tableau, Python, R, and JavaScript. I will introduce and explain all the code we need for the class meetings. If you want to use a technology in your assignments but do not know it yet, the following resources will help you to get up to speed.
Tableau:
Python, Jupyter, R, and D3.js:
I am committed to your learning success. Please feel free to contact me with any questions regarding this course. If I am not able to help you myself, I will forward your request to someone who can.
Class meetings are Saturdays, 8:30 AM to 11:15 AM in Lucas Hall 210.
This course is centered around a practical and reflective approach to data visualization. Each class meeting will have the same structure:
Each class meeting will focus on one specific design principle in data visualizations and one aspect of visualization technology. During the lab session, you have the opportunity to work on a data visualization case study that allows you to practice the application of the design principle and technology. At the end of each lab session, you may be asked to present your results.
“What it boils down to is one per cent inspiration and ninety-nine per cent perspiration.” (Thomas Edison)
Your mastery of the learning objectives will be examined through contributions to a class reader, an individual project, and a team project. There will be no exams.
The following table links the learning objectives of this class with the assignments and shows the maximum number of points that you can achieve with each assignment towards the final grade.
| Learning Objective | Assignment | Max. Points |
|---|---|---|
| Understand the conceptual and technical fundamentals of data visualization. | Class Reader | 30 |
| Analyze and critique the persuasiveness and evidence of existing data visualizations | Individual Project | 40 |
| Implement persuasive and evidence-based visualizations | Team Project | 30 |
| Total | 100 |
The final grade distribution is as follows.
| Points | Letter Grade |
|---|---|
| 100-94 | A |
| >94-90 | A- |
| >90-87 | B+ |
| >87-84 | B |
| >84-80 | B- |
| >80-77 | C+ |
| >77-74 | C |
| >74-70 | C- |
| >70-0 | F |
My grading criteria are as follows:
I reserve the right to change the grading to accommodate special circumstances and opportunities. Any changes, however, will be discussed and announced in class and on Camino.
The class reader is the virtual extension of the classroom. You use the class reader to collaboratively develop a deeper understanding of the conceptual and technical fundamentals of data visualization.
Your objective is to contribute in a meaningful way to the class reader on a weekly basis. Meaningful contribution is defined as the following set of activities:
When contributing to the class reader, make sure that you understand the requirements of academic integrity as outlined below.
The tentative structure of the class reader is as follows:
In the spirit of great examples of collaborative writing, we use github to organize the writing process. You will use branches, projects, issues, pull requests, and wikis to manage your work efficiently.
I will evaluate your weekly contribution to the class reader based on the following criteria.
| Criteria | Metrics | Max. Points |
|---|---|---|
| Quantitative Activity | Commits, Additions, Deletions, Issue Handling, Wiki Contributions | 1 |
| Qualitative Activity | Quality of Content, Arguments, and Reflection | 2 |
| Total | 3 |
Additionally, twice in the quarter (on May, 12 and June, 9), I will also evaluate you based on the following criteria.
| Criteria | Metrics | Max. Points |
|---|---|---|
| Contributions to Organization | Structure, Composition, and Logical flow | 2 |
| Contributions to Professionality | Style, Form | 1 |
| Total | 3 |
I will grade you based on the results in the class reader github repository on Saturday, 12:00pm each week.
You pursue two objectives with the individual project:
The topic for the individual project is Gun Violence in the United States. We use the following data product: https://www.vox.com/policy-and-politics/2017/10/2/16399418/us-gun-violence-statistics-maps-charts (A PDF version is on Camino in case the website becomes unavailable or changes significantly).
The following table provides an overview of the deliverables for the individual project.
| Project Phase | Due | Max. Points |
|---|---|---|
| Problem Statement | April, 21 2018 (1:00 PM) | 5 |
| First Version “Redesign” | May, 5 2018 (1:00 PM) | 10 |
| First Version “Deception” | May, 19 2018 (1:00 PM) | 10 |
| Revised Versions | June, 2 2018 (1:00 PM) | 15 |
| Total | 40 |
PLEASE NOTE: It is vital for you to start early and discuss intermediate results with me. I will not accept late submission without prior notice or without a doctor’s note. I am aware that sometimes life goes crazy but please notify me in advance and we will work it out.
The problem statement should outline your initial (and perhaps intuitive) analysis of the data product. Think about the following questions:
I will evaluate your problem statement based on the following criteria.
| Criteria | Metrics | Max. Points |
|---|---|---|
| Content | Understandability (0.5), Completeness (0.5) | 1 |
| Persuasiveness | Clarity (1), Argumentation (1) | 2 |
| Evidence | Sources (1) | 1 |
| Style | Professionality (0.5), Originality (0.5) | 1 |
| Total | 5 |
The first versions of your redesigned data product and your deceptive data product document your prototypes. Both versions should achieve the following:
I will evaluate both versions based on the following criteria:
| Criteria | Metrics | Max. Points |
|---|---|---|
| Finding 1 | Persuasiveness (1), Evidence (1) | 2 |
| Finding 2 | Persuasiveness (1), Evidence (1) | 2 |
| Finding 3 | Persuasiveness (1), Evidence (1) | 2 |
| Making-of | Structure (1), Reproducibility (1) | 2 |
| Style | Creativity (1), Professionality (1) | 2 |
| Total | 10 |
The revised versions of the redesigned data product and the deceptive data product document your individual mastery of the course. Both redesigned versions should substantially improve the first versions and include:
The final version must be one reproducible and self-contained online deliverable (a Jupyter notebook, an R notebook, an Observable notebook, a webpage, etc.).
I will evaluate the result of this phase based on the following criteria:
| Criteria | Metrics | Max. Points |
|---|---|---|
| Improvement Redesign | Persuasiveness (1), Evidence (1), Structure (1), Reproducibility (1) | 4 |
| Polishing Redesign | Application of Class Concepts (1), Professionality (1) | 2 |
| Improvement Deception | Persuasiveness (1), Evidence (1), Structure (1), Reproducibility (1) | 4 |
| Polishing Deception | Application of Class Concepts (1), Professionality (1) | 2 |
| Integration | Creativity (1), Effort (1), Organization (1) | 3 |
| Total | 15 |
The objective of the team project is to collaboratively develop a data product. A data product tells a complex story using several data visualizations. You will work teams of up to five students. Each team is free to choose a topic of interest. The challenge of a team project is to organize your team, hold one another accountable, and complement your skills and interests. At the end of the team project, your teammates will evaluate your contributions to the project. This may influence your grade for the team project.
The following table provides an overview of the deliverables for the team project.
| Project Phase | Due | Max. Points |
|---|---|---|
| Project Statement | April, 28 2018 (1:00 PM) | 5 |
| Exploratory Data Analysis | May, 12 2018 (1:00 PM) | 5 |
| First Version | May, 26 2018 (1:00 PM) | 5 |
| Presentation | June, 9 2018 (8:30 AM) | 5 |
| Revised Versions | June, 14 2018 (11:59 PM) | 10 |
| Total | 30 |
PLEASE NOTE: It is vital for you to start early and discuss intermediate results with me. I will not accept late submission without prior notice or without a doctor’s note. I am aware that sometimes life goes crazy but please notify me in advance and we will work it out.
The problem statement should outline your team project and your project plan. Think about the following questions:
I will evaluate your problem statement based on the following criteria.
| Criteria | Metrics | Max. Points |
|---|---|---|
| Content | Understandability (1), Completeness (1) | 2 |
| Persuasiveness | Clarity (.5), Argumentation (.5) | 1 |
| Evidence | Sources (1) | 1 |
| Style | Professionality (0.5), Originality (0.5) | 1 |
| Total | 5 |
During the exploratory data analysis your objective is twofold. First, you should collect, clean, and integrate data. Second, you establish a thorough understanding of the content and the limitations of your data.
The exploratory data analysis must:
I will evaluate your exploratory data analysis based on the following criteria.
| Criteria | Metrics | Max. Points |
|---|---|---|
| Data description | Understandability (1), Completeness (1) | 2 |
| Data preparation & use | Clarity (1), Explanations (1) | 2 |
| Style | Professionality (1) | 1 |
| Total | 10 |
In this phase you will develop the first version of your data product. You should achieve the following:
I will evaluate both versions based on the following criteria:
| Criteria | Metrics | Max. Points |
|---|---|---|
| Narrative | Persuasiveness (1), Evidence (2) | 3 |
| Style | Creativity (1), Professionality (1) | 2 |
| Total | 5 |
The in-class presentation should achieve the following:
I will evaluate both versions based on the following criteria:
| Criteria | Metrics | Max. Points |
|---|---|---|
| Progress | Persuasiveness (2), Evidence (1) | 3 |
| Style | Creativity (1), Professionality (1) | 2 |
| Total | 5 |
The final data product must be online (e.g., on github) by the deadline. The final data product should consist of two items:
I will evaluate the revised versions based on the following criteria:
| Criteria | Metrics | Max. Points |
|---|---|---|
| Progress | Improvements in Problem Statement (1), Improvements in Data Analysis (1), Improvements in Narrative (1), Improvements in Evidence (1) | 4 |
| Video | Content (1), Effectiveness (1), Originality (1) | 3 |
| Professionality | Style (1), Structure (1), Polishing (1) | 3 |
| Total | 10 |
I firmly believe that mastery of data visualization requires constant practice. You will ace this course if you:
The Design Principle column contains the introductory reading for the class meetings.
| Week | Class Meeting | Foundations | Design Principle | Lab Session | Reader | Individual Project | Team Project |
|---|---|---|---|---|---|---|---|
| 1 | April, 14 | Introduction | Overview | Hello, viz world! | - | - | - |
| 2 | April, 21 | Analytic Design | The Visualization Zoo | Tableau | R1 (3) | IP1 (5) | - |
| 3 | April, 28 | Toulmin’s Argumentation Model | Grammar of Graphics | Tableau | R2 (3) | - | TP1 (5) |
| 4 | May, 5 | Goal, Question, Metrics (GQM) | Data Semantics | P4DA | R3 (3) | IP2 (10) | - |
| 5 | May, 12 | The Audience Model | Data Actions | Altair | R4 (6) | - | TP2 (5) |
| 6 | May, 19 | Situational Awareness | Marks, Channels & Color | R and Tidyverse | R5 (3) | IP3 (10) | - |
| 7 | May, 26 | The Data Pixel Ratio | Rules of Thumb | ggplot2 & Shiny | R6 (3) | - | TP3 (5) |
| 8 | June, 2 | The Truth Continuum | Deception Techniques | D3.js | R7 (3) | IP4 (15) | - |
| 9 | June, 9 | Review | Review | D3.js | R8 (6) | - | TP4 (5) |
| 10 | June, 14 | - | - | - | - | - | TP5 (10) |
| Total = 100 points | 30 | 40 | 30 |
The Academic Integrity pledge is an expression of the University’s commitment to fostering an understanding of and commitment to a culture of integrity at Santa Clara University. The Academic Integrity pledge, which applies to all students, states:
“I am committed to being a person of integrity. I pledge, as a member of the Santa Clara University community, to abide by and uphold the standards of academic integrity contained in the Student Conduct Code.”
You are expected to uphold the principles of this pledge for all work in this class. For more information about Santa Clara University’s academic integrity pledge and resources about ensuring academic integrity in your work, see www.scu.edu/academic-integrity.
In particular, I expect that you give credit to any material (including but not limited to journal articles, web article, blog posts, images, data sets, and any media) that you have used for completing any assignment in this class. Being able to give credit by referencing sources consistently and correctly is evidence of mastery of a topic. It shows that you are able to construct original arguments that are backed with verifiable evidence. Failing to give credit is a sign of an inadequate learning progress. It shows that you have not understood the topic well enough to formulate your own arguments in relation to already existing ideas.
During your work in this class, you will use, modify, or extend digital content that you have found online. You will also use libraries, APIs, code snippets, and data sets that have been created by others. In every piece of work (presentations, assignments, etc.), you must acknowledge work, source code, data sets, and any other content that was not produced by you. Acknowledgements must be easily identifiable, inseparable from your content, and must not violate licenses.
Failure to provide appropriate acknowledgements will result in an F grade for that assignment. Repeated failure to provide appropriate acknowledgements will result in an F grade for the entire course.
During the first class, we will discuss this digital content policy. After this class, I will strictly enforce this policy. If you have doubts, contact me.
I will support you in your learning in this class and beyond to the best of my abilities. If I am not able to help you myself, I will identify someone who can. I will evaluate your contribution solely based on the standards set by this syllabus. Changes to the syllabus will be highlighted, discussed during class sessions, and will be published on Camino.
By enrolling in this class, you agree to the requirements stated in this syllabus. You will operate with integrity in your dealings with me and your fellow students. You will engage the learning materials with appropriate attention and dedication and maintain their engagement when challenged by difficult learning activities. You will contribute to the learning of others and you will perform to standards set by this syllabus.
Mutual respect is the foundation of this course. No one will be criticized for being wrong. Appropriate conduct includes honesty, self-respect, respect for others, and compliance with university policies and standards. Computers in the classroom should be used only for completing course-related work and for taking notes; cell phones must be turned off or muted.
Please let me know via email during the first two weeks of the course if you have any conflicts between a course element (class meeting, assignment) and another vital commitment (another course, work, university-related extracurricular activities, religious commitments). At my discretion, I will you provide with alternative means to complete the course element.
I am aware that many of you have multiple commitments. You should attend at least 80 percent of all scheduled class meetings. If you miss more than 20 percent of scheduled classes, you will receive reduction by one letter grade.
If you have a disability for which accommodations may be required in this class, please contact Disabilities Resources (Benson Hall 216, 408-554-4109) as soon as possible to discuss your needs and register for accommodations with the University. If you have medical needs related to pregnancy, you may also be eligible for accommodations. If you have already arranged accommodations through Disabilities Resources, please discuss them with me during my office hours as soon as possible.
While I am happy to assist you, I am unable to provide accommodations until I have received verification from Disabilities Resources. If you are in doubt of whether you are eligible for accommodations, I encourage you to contact Disabilities Resources (Benson Hall 216, 408-554-4109). The Disabilities Resources office would be grateful for advance notice of at least two weeks.
In alignment with Title IX of the Education Amendments of 1972, and with the California Education Code, Section 66281.7, Santa Clara University provides reasonable accommodations to students who are pregnant, have recently experienced childbirth, and/or have medically needs related to childbirth. Pregnant and parenting students can often arrange accommodations by working directly with their instructors, supervisors, or departments. Alternatively, a pregnant or parenting student experiencing related medical conditions may request accommodations through Disabilities Resources (Benson Hall 216, 408-554-4109).
Santa Clara University upholds a zero-tolerance policy for discrimination, harassment and sexual misconduct. If you (or someone you know) have experienced discrimination or harassment, including sexual assault, domestic/dating violence, or stalking, I encourage you to tell someone promptly. For more information, please consult the University’s Gender-Based Discrimination and Sexual Misconduct Policy at http://bit.ly/2ce1hBb or contact the University’s EEO and Title IX Coordinator, Belinda Guthrie, at 408-554-3043, bguthrie@scu.edu. Reports may be submitted online through https://www.scu.edu/osl/report/ or anonymously through Ethicspoint https://www.scu.edu/hr/quick-links/ethicspoint/
This syllabus was inspired by Aleszu Bajak’s syllabus, Jeffrey Shaffer’s data visualization with Tableau course, and earlier versions of CS171 at Harvard.
Loosely based on Naisbitt, J. 1982: Megatrends, Warner Books↩